Designing Paste Thickeners for Copper Flotation Tailings, Using Bed depth Scale-up Factor
Bibliographic record
Abstract
The paste thickener could increase the water recovery and reduce the environmental impacts in tailings dam. The present work aimed to find the appropriate scale-up factor for bed depth to design paste thickener for copper tailings, using a lab glass cylinder and an operating pilot column. The thickening tests were carried out on the flotation tailing samples obtained from the Sarcheshmeh and Miduk copper mines located in Iran. Based on the industrial conditions, the values of influential parameters for paste thickener used in these experiments were pH=11 and feed solid=10 %. Flocculant type was NF43U and used as 25g/t with dosage of 0.25 gt. The unit area of Sarcheshmeh and Miduk paste thickeners were designed as 0.057 and 0.047m2/t/day, respectively. Based on the dry feed rates to each paste thickener as 7920 and 4320 t/day, the thickener's diameters were determined as 23.9 and 16.1 meters which are similar to the actual thickener’s diameters (24 and 16 meters), respectively. In addition to unit area, the bed depth is also important in the paste thickeners design. Hence, the ratio of industrial to lab unit bed volume for Sarcheshmeh paste thickener was obtained 75 which was equal to the ratio of industrial to lab bed depth (bed depth scale-up factor exclusively for copper flotation tailings). This procedure was validated by using the Miduk sample. The bed depth in the paste thickeners was determined as 7.5 meters, by using the bed depth scale-up factor which was comparable to the actual bed depth (8 meters). This research confirmed that the bed depth scale-up factor is able to correctly determine the bed depth of industrial paste thickeners for copper tailings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".